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Estimation of Forces and Powers in Ergometer and Scull Rowing Based on Long Short-Term Memory Neural Networks.

Lorenzo Pitto1,2, Frédéric R Simon1,2, Geoffrey N Ertel1,2

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Summary

This study introduces a cost-effective method for analyzing rowing performance, using sensors to estimate force and power output. The new technique accurately identifies technique differences, aiding athlete monitoring.

Keywords:
IMULSTMkineticsmachine learningrowing

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Area of Science:

  • Sports Science
  • Biomechanics
  • Performance Analysis

Background:

  • Rowing performance analysis is crucial for athletes and coaches.
  • Current methods using specialized equipment (PowerLine, BioRow) are expensive and time-consuming.
  • There is a need for more accessible and affordable performance monitoring tools.

Purpose of the Study:

  • To develop a simpler and more cost-effective method for estimating rowing force and power output.
  • To validate the accuracy of the new method in distinguishing between different rowing techniques.

Main Methods:

  • Utilized cable position sensors for ergometer rowing.
  • Employed inertial measurement units (IMUs) and GPS for scull rowing.
  • Trained a long short-term memory (LSTM) network on data from 12 ergometer and 11 scull rowers.

Main Results:

  • The LSTM network accurately reconstructed rowing forces and power with a mean absolute error under 5%.
  • The reconstructed data revealed inter-subject technique differences with 93% accuracy.
  • Leave-one-out validation indicated a need for more data for broader generalizability.

Conclusions:

  • The developed method offers a cheaper and easier alternative for rowing performance analysis.
  • The technique shows promise for identifying subtle differences in rowing mechanics.
  • Further research with larger datasets is recommended to enhance the method's generalizability.